mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
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66 lines
2.3 KiB
Python
66 lines
2.3 KiB
Python
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from paddle import nn
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from ppocr.modeling.transforms import build_transform
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from ppocr.modeling.backbones import build_backbone
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from ppocr.modeling.necks import build_neck
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from ppocr.modeling.heads import build_head
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from .base_model import BaseModel
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from ppocr.utils.save_load import load_dygraph_pretrain
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__all__ = ['DistillationModel']
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class DistillationModel(nn.Layer):
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def __init__(self, config):
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"""
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the module for OCR distillation.
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args:
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config (dict): the super parameters for module.
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"""
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super().__init__()
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freeze_params = config["freeze_params"]
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pretrained = config["pretrained"]
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if not isinstance(freeze_params, list):
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freeze_params = [freeze_params]
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assert len(config["Models"]) == len(freeze_params)
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if not isinstance(pretrained, list):
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pretrained = [pretrained] * len(config["Models"])
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assert len(config["Models"]) == len(pretrained)
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self.model_dict = dict()
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index = 0
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for key in config["Models"]:
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model_config = config["Models"][key]
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model = BaseModel(model_config)
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if pretrained[index] is not None:
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load_dygraph_pretrain(model, path=pretrained[index])
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if freeze_params[index]:
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for param in model.parameters():
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param.trainable = False
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self.model_dict[key] = self.add_sublayer(key, model)
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index += 1
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def forward(self, x):
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result_dict = dict()
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for key in self.model_dict:
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result_dict[key] = self.model_dict[key](x)
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return result_dict
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